RTX 5080 vs T4

The RTX 5080 delivers 112.6 TFLOPS dense FP16 against 56.3 TFLOPS FP32 for a ratio of exactly two. The T4 delivers 65 TFLOPS dense FP16 against 8.1 TFLOPS FP32 for a larger ratio. Training workloads that rely on dense FP16 therefore execute faster on the RTX 5080 because the absolute dense FP16 number is higher. Inference workloads that stay in dense FP16 likewise complete more operations per second on the RTX 5080. Memory bandwidth of 960 GB/s on the RTX 5080 versus 320 GB/s on the T4 permits larger batch sizes before memory capacity or transfer limits are reached. When sparsity is enabled the RTX 5080 reaches 225.1 TFLOPS sparse FP16 while the T4 figure remains unavailable. Direct comparison of like metrics therefore favors the RTX 5080 for both dense and sparse FP16 workloads.

The RTX 5080 suits LLM training and fine tuning because its 112.6 TFLOPS dense FP16 exceeds the T4 value by a wide margin. Scientific computing tasks that need 56.3 TFLOPS FP32 also finish sooner on the RTX 5080. Higher 960 GB/s bandwidth further supports larger batches in these workloads.

The T4 suits power constrained environments because its 70W TDP is far below 360W. Inference workloads that fit within 65 TFLOPS dense FP16 and 130 TOPS dense INT8 can run on the T4 when energy limits dominate over speed. The lower bandwidth of 320 GB/s remains acceptable for small batch inference.